Duke AI Health congratulates Chief AI Health Scientist Ricardo Henao, PhD, on his promotion to the rank of Associate Professor in the Department of Biostatistics and Bioinformatics in the Duke University School of Medicine. Dr. Henao is a major presence in health data science at Duke, where his leadership and expertise in machine learning methods and implementation have made him a sought-after collaborator and instructor. “Dr. Henao is a major asset to Duke AI Health and to the larger Duke community,” said Michael Pencina, PhD, director of Duke AI Health and vice dean for data science at the School of Medicine. “We feel fortunate to be able to benefit from such a rare combination of talent and knowledge spanning research, application, and teaching.”
Duke AI Health Director and Vice Dean for Data Science Michael J. Pencina, PhD, has achieved a major academic milestone: according to Google Scholar’s analytics, he has recently passed the 100,000 mark for academic citations of his work. Pencina, who in addition to his leadership role in Duke’s efforts to develop, evaluate, and implement ethical and equitable data science, has also worked extensively on the development and evaluation of risk prediction models and clinical trial designs.
As a member of the Coalition for Health AI, Duke AI Health is working to develop a consensus-driven framework to drive high-quality health care through the adoption of credible, fair, and transparent health AI systems. The coalition is convening a series of virtual workgroup sessions to define core principles and has published a white paper from its first meeting: “Bias, Equity, and Fairness.” Please review the paper and submit your feedback by Sept. 15: https://bit.ly/3wbAXQx. With the help of your ideas, the Coalition for Health AI can advance towards establishing clear and appropriate guidelines and guardrails for the fair, ethical, and useful application of AI and machine learning in health care settings.
Much-Touted Genomic Test Score Shows Minimal Utility in Study Led by AI Health Director Michael Pencina
New research led by Duke AI Health Director Michael Pencina, PhD, published recently in the journal Circulation, looked at the value of using a genomic test to predict the future risk of heart disease. Pencina and colleagues found that the genomic test, referred to as the polygenic risk score (PRS), only marginally added to the predictive information obtained through the assessment of traditional risk factors, concluding that the PRS “had minimal clinical utility”.
We invite Duke students to apply for the Health Data Science (HDS) fall research program. This competitive program, based in Duke AI Health, is designed to allow students who have previous experience in data science to continue their engagement with substantive applied projects. The HDS Research Program offers Duke students, both undergraduate and graduate, the opportunity to be a part of research teams applying advanced machine learning (deep learning) to important areas of medicine. Participating students will be mentored by leading Duke faculty involved in data science research, often with guidance by practicing clinicians. The fall will culminate in a showcase session where student teams will present their results.
A group of Duke Health researchers recently shared their insights on approaches to managing the complex issues that are emerging as “algorithmic medicine” increasingly becomes part of clinical care at hospitals and health systems. The authors, who comprise faculty and staff from Duke AI Health, Duke Health Technology Solutions, the Duke Institute for Health Innovation, and other physicians and researchers from Duke University and Duke University Health System, published an account of their approach to evaluating and monitoring the use of algorithmic predictive models at Duke Health hospitals and clinics. The article, titled “A framework for the oversight and local deployment of safe and high-quality prediction models,” was published on May 31 in the Journal of the American Medical Informatics Association (JAMIA). It showcases the processes and procedures by which an expert group at Duke Health known as Algorithm-Based Clinical Decision Support (ABCDS) Oversight reviews, approves, and manages predictive models intended for use in patient care settings.
Can AI safely automate medical decision-making tasks to improve patient outcomes? In this talk, the presenters will share the challenges in the development and translation of medical AI, and how they are being addressed through a blend of innovation in algorithm development, dataset curation, and implementation design. They will first talk about self-supervised learning methods for medical image classification that leverage large unlabeled datasets to reduce the number of manual annotations required for expert-level performance. Then, they will discuss open benchmarks that can help the community transparently measure advancements in generalizability of algorithms to new geographies, patient populations, and clinical settings. Third, they will share insights from studies that investigate how to optimize human-AI collaboration in the context of clinical workflows and deployment settings. Altogether, this talk will cover key ways in which we can realize the potential of medical AI to make healthcare more accurate, efficient and accessible for patients worldwide.
The Duke+Data Science program is pleased to announce the Duke Machine Learning Summer School 2022, offered in June as a live five-day class that provides lectures on the fundamentals of machine learning. The curriculum in the MLSS is targeted to individuals interested in learning about machine learning, with a focus on recent deep learning methodology. The MLSS will introduce the mathematics and statistics at the foundation of modern machine learning, and provide context for the methods that have formed the foundations of rapid growth in artificial intelligence (AI).
We invite Duke students to apply for the Health Data Science (HDS) summer research program. This competitive program, based in Duke AI Health, is designed to allow students who have previous experience in data science to continue their engagement with substantive applied projects. The Advanced Machine Learning Projects in Health Data Science offers Duke students, both undergraduate and graduate, the opportunity to be a part of research teams applying advanced machine learning (deep learning) to important areas of medicine. Participating students will be mentored by leading Duke faculty involved in data science research, often with guidance by practicing clinicians. The summer will culminate in a showcase session where student teams will present their results.
Duke AI Health welcomes Maciej Mazurowski, PhD, who will join its Faculty Council as Director of Radiology Imaging. At AI Health, Dr. Mazurowski will coordinate the AI Health Initiative for Medical Imaging. This new effort will engage experts in machine learning and clinical medicine from across Duke’s campus to foster and accelerate the development, validation, and clinical implementation of machine learning algorithms for medical imaging. “I’m excited to undertake this new challenge and I’m looking forward to working with experts and leadership across the entire campus to build on existing technical and clinical strengths in medical imaging AI at Duke,” Dr. Mazurowski said.
The mission of Duke AI Health is to enable the discovery, development, and implementation of artificial intelligence (AI) at Duke and beyond. A key component to achieving this goal is to foster high-impact, rigorous, and competitive proposals for scientific awards. The 2022 AI Health Proposal Studios will provide a structured opportunity for investigators to engage with Duke’s top data science expertise and thought leadership, and to receive review and feedback of the scientific components of their proposals. After seeing a strong response to the Proposal Studio concept and the following virtual learning experiences in 2021, AI Health plans to continue building on last year’s success with the overarching goal of fostering high-impact, rigorous, and competitive proposals for scientific awards.
In a recent post at the Tableau blog, the data visualization company praises the Duke Analytics Community (DAC) for the group’s commitment to “taking data democracy to (the) next level.” The post, which is available at the Tableau website, singled out the Duke Cancer Institute’s Claire Howell and Duke University’s Rebecca McDaniel for recognition based on their initiative in helping to create a “department-agnostic space” where users of analytics software across the School of Medicine and Health System could share ideas and improve data access.
Duke AI Health welcomes its first AI Health Equity Scholar, Michael P. Cary, PhD, RN, who is now beginning a yearlong scholarship supported by Duke AI Health and the Duke Clinical & Translational Science Institute. The AI Health Equity Scholars Program, which provides funding for Duke University faculty, staff, and postdoctoral scholars to actively collaborate with AI Health leadership, is focused on broadening Duke’s commitment to ethical and equitable data science and artificial intelligence (AI) in health applications.
Duke AI Health is pleased to launch the AI Health Data Studio Seminar series this spring. This multi-part educational offering is designed for campus-based researchers at Duke who are interested in working with medical data but are unsure where to begin. Hosted by Senior Informacist Ursula Rogers, Chief AI Health Scientist Ricardo Henao, PhD, and Associate Director of Informatics Shelley Rusincovitch, MMCi, the series will feature data experts from across the Duke enterprise.Campus-based researchers are especially invited to attend along with anyone interested from the Duke community, including faculty, staff, and students.
Duke AI Health and the Duke Clinical & Translational Science Institute are pleased to announce a call for applications with the AI Health Equity Scholars Program. This program will support a minimum 1-year appointment for a faculty member, staff member, or postdoctoral scholar at Duke University. The AI Health Equity Scholars Program is a new initiative intended to broaden our commitment to ethical and equitable data science and artificial health (AI) applications, with direction from CTSI Director L. Ebony Boulware, MD, MHS, and AI Health Director Michael J. Pencina, PhD. The intention of this program is to broaden our expertise in considering and applying ethical and equitable principles for key initiatives within Duke AI Health. Applications must be submitted by Friday, December 10, 2021 by 10 PM (Eastern Time).
Given the rapid growth in and importance of harnessing health data as a tool, Mary Klotman, MD, Dean, Duke University School of Medicine, recently announced the key leadership appointment of Michael Pencina, PhD, Vice Dean for Data Science for the School of Medicine, as the Director of Duke AI Health effective October 13, 2021. Designed as a multidisciplinary initiative, AI Health intends to unlock the enormous opportunity to spur collaborations that will leverage knowledge and expertise from across campus.
Duke AI Health and the Duke Clinical & Translational Sciences Institute are pleased to announce a call for applications to the Spring 2022 Clinical Research with Electronic Health Records Data (CR-EHR) Course, with a November 19, 2021 application submission deadline. CR-EHR is an interdisciplinary course designed to engage both clinical and quantitative researchers in learning how to access and work with Duke EHR data. Data captured in the Duke EHR represent the broad spectrum of patient care delivered by Duke Health, which can be leveraged for a variety of research questions and study designs. Clinical trainees will develop a deeper understanding of the types of analytic studies that can be conducted with EHR data, while quantitative trainees will develop a deeper knowledge base for how to query and process EHR data.
Now more than ever, clinicians can access an incredible amount of data about their patients. Electronic health records (EHRs) offer a massive repository of information about each individual: notes of all kinds, laboratory results, imaging data, scanned forms, and saved images. Soon, we may even be able to add data from wearable devices such as personal fitness trackers into the mix. However, this breadth of information can be both a blessing and a curse. Clinicians can learn more about their patients from the medical chart than was previously possible—but only if they are able to rapidly and accurately sort through that information and find the most relevant points for a given clinical encounter.
This December, Duke AI Health Director and Professor of Biostatistics and Bioinformatics Michael Pencina will join a group of experts for a panel discussion hosted by the Duke Alumni Forever Learning Institute called “Artificial Intelligence: Capabilities, Liabilities, and Responsibilities.” The discussion, the final installment in a four-part series taking place this fall titled “Artificial Intelligence: Real Ethical Quandaries,” will focus on the expanding role of artificial intelligence in decision-making and the practical and ethical issues that can arise from the use of a technology whose inner workings are often opaque and whose operations can be affected by built-in biases. Panel participants will examine how these technologies are being used in arenas such as medicine and national security and discuss the potential impacts of these tools, both positive and negative, on people’s daily lives. The session will take place as an online Zoom webinar on Tuesday, December 7, 2021, from 3:00-4:00 PM Eastern time, and will be moderated by Duke Law Professor and Director of the Duke Initiative for Science and Society Nita Farahany.
Eric Perakslis, PhD, DCRI’s Chief Science & Digital Officer, will present at DEF CON 2021 in a talk called “Truth, Trust, and Biodefense.” Learn more about his presentation in his blog post for the DCRI below:
“On May 12, 2017, a ransomware cyberattack known as WannaCry was launched. Within a day, it was raging worldwide and had infected tens of thousands of computers and electronic devices belonging to the United Kingdom’s National Health Service, causing severe disruptions to hospital operations. Shortly after 15:00 UTC on May 13, the infection was halted when information security researcher and hacker Marcus Hutchins discovered and exploited a “kill switch” embedded in the malware’s code. In addition to greatly slowing WannaCry’s spread, this kill switch also prevented infected computers from being encrypted and their data locked. Marcus Hutchins’ story is notably complex, but there is no denying that his actions greatly decreased the global harm that likely would have otherwise occurred. The term hacker often brings to mind a faceless, hooded figure that is ubiquitously linked to crime. Given how pervasive this image is, it may surprise some to learn that there are many “good” hackers. This distinction is made especially clear in the viral TED Talk given by cybersecurity Keren Elazari titled “Hackers: the internet’s immune system.” In this talk, Elazari argues that hackers make the internet stronger by testing its defenses, which forces the internet to adapt, improve, and strengthen, not unlike the body’s adaptive immune system.
Duke AI Health Director Michael Pencina, PhD, who is a professor of biostatistics and bioinformatics at Duke and serves as the medical school’s vice dean for data science and information technology, was recently quoted in an article appearing in STAT News examining the use of commercially developed predictive algorithms in medicine. In an investigative report for STAT News, correspondent Casey Ross spoke with employees in multiple health systems across the country that use clinical algorithms created by Epic, one of the nation’s largest electronic health record vendors.
Duke+DataScience (+DS) is a Duke-wide educational initiative devoted to expanding knowledge of and facility with machine learning and other artificial intelligence tools across multiple academic fields, including the arts, humanities, and social sciences as well as medicine and quantitative sciences. With an extensive and growing curriculum that includes both online and in-person courses in neural networks, natural language processing, deep learning, and other machine learning applications, +DS offerings span learning needs ranging from novice to expert and are tailored to specific academic and professional applications.
Duke’s +Data Science (+DS) recently concluded its 2021 Machine Learning Virtual Summer School (MLvSS). This event, the ninth machine learning school held since 2017, sold out more than a month in advance and completely filled a 100-person waitlist. This high demand reflects both the substantial demand for instruction in methods driving the rapid growth in artificial intelligence, as well as a keen interest in tapping into high-quality instruction from Duke teachers with expertise in the mathematics and statistics that underlie modern machine learning methods.
Keeping up with the pace of research in health data science is challenging at the best of times, and the COVID-19 pandemic has not made things any easier. For this reason, Duke AI Health and the Duke +Data Science (+DS) program worked together this spring to launch the Proposal Studio Virtual Learning Experiences (vLE). The Proposal Studios sessions were designed to help investigators develop effective, successful proposals for research project involving health data science. From March through April of 2021, +DS held four successful proposal studios, assisting 13 investigators to develop scientific proposals. Open to anyone within the Duke community, the series attracted a total of 129 attendees and averaged 32 audience members per vLE.
Duke Rheumatologists Explore the Effects of a Rapid Transition to Telemedicine During the COVID-19 Pandemic
The COVID-19 pandemic has prompted a surge in demand for telehealth services, but many questions about how healthcare providers can adapt their practice to meet the challenges of telemedicine remain to be answered. Now, a group of rheumatologists at Duke University School of Medicine have used data drawn from the Duke University Health System’s EHRs (electronic health records) to investigate how a rapid transition to telemedicine affected their approach to patient care.
A group of neuroscientists and machine learning experts are developing new ways to analyze animal movement and behavior to gain insights into the inner workings of the nervous system. Combining expertise from the disciplines of neurobiology and artificial intelligence, a team of researchers from Duke University, Harvard, MIT, Rockefeller University, and Columbia University have developed a system that captures detailed, multiple-view video of animals in their natural environment, and then uses data from those video images to build a detailed model of how the animal moves. This allows scientists to use movement and behavior as a window into brain function.
Ursula Rogers, senior informaticist with Duke Forge and AI Health, recently presented a poster at the American Medical Informatics Association (AMIA) 2021 Virtual Informatics Summit. The poster, “Enabling Data Liquidity for Health Data Science: A Suite of APIs for EHR Data” discusses an ongoing partnership between the Duke Health Technology Solutions (DHTS) Analytic Center of Excellence and AI Health. 18 application programming interfaces (API) have been developed to provide efficient and secure programmatic access to electronic health record (EHR) data for machine learning.
Since it was declared a global pandemic in March 2020, COVID-19 upturned university and college campuses across the United States, causing major disruption to student life. As Duke’s campus went into a full lockdown following a steep uptick in COVID-19 infections in North Carolina last spring, Duke’s Harshavardhan (Harsha) Srijay, a 19-year-old second-year undergrad student majoring in math and data science, saw his plans for the 2020 summer crumble. As prior opportunities fell through the cracks, the Duke Plus Data Science (+DS) Advanced Projects summer program provided him a platform to not only be engaged and productive through a very difficult summer, but also come out of it with a successful project that he recently presented at the American Medical Informatics Association (AMIA) 2021 Virtual Informatics Summit(link is external).
In this one-hour virtual learning experience, 3 teams of Duke investigators will discuss their proposal concepts with data science experts. For April 5, proposal concepts will include genomic analysis related to sickle cell anemia, lifestyle intervention adherence, and transplant optimization. The proposal studio vLE concept is newly launching in spring 2021, with the goal of assisting Duke investigators with proposal development in health data science, and in sharing experiences with the broader Duke community. The series is co-hosted by Duke AI Health and the Duke+Data Science (+DS) program.
Now Accepting Proposals for Placement of a Pathology AI Health Fellow for Projects within the Department
AI Health is currently considering requests for placement of a Pathology AI Health Data Science Fellow. The AI Health Data Science Fellowship is a 2-year training program in data science with direct application for healthcare. The Pathology AI Health fellow will be funded jointly by AI Health and the Department of Pathology. Fellows will also receive support from AI Health and the Duke Department of Biostatistics and Bioinformatics, with overall program supervision provided by the Duke Clinical Research Institute’s Center for Predictive Medicine.
Now Accepting Proposals for Placement of a Microsoft–Duke AI Health Fellow for Projects within the School of Medicine
AI Health is currently considering requests for placement of a Microsoft-Duke AI Health Data Science Fellow for projects proposed by Departments/Divisions within the Duke University School of Medicine. The Microsoft–Duke AI Health Data Science Fellowship is a 2-year training program in data science with direct application for healthcare. Funded in part by a grant from the Microsoft Corporation, Microsoft-Duke AI Health Fellows will also receive support from AI Health, the clinical divisions to whose projects they are assigned, and the Duke Department of Biostatistics and Bioinformatics, with overall program supervision provided by the Duke Clinical Research Institute’s Center for Predictive Medicine.
The mission of Duke AI Health is to enable discovery, development, and implementation of artificial intelligence (AI) at Duke and beyond. A key component to achieving this goal is to foster high-impact, rigorous, and competitive proposals for scientific awards. The AI Health Proposal Studios will provide a structured opportunity for investigators to engage with Duke’s top data science expertise and thought leadership, and to receive review and feedback of the scientific components of their proposals. The deadline for submitting applications is 5:00 PM Eastern time on Monday, December 7, 2020.
A Machine Learning for Mobile Health workshop, part of the upcoming Neural Information Processing Systems Conference (NeurIPS 2020), is inviting contributions and extended abstracts from researchers and clinicians in the interdisciplinary machine learning and mobile health space, with the goal to better address the various challenges currently facing the widespread use of mobile health technologies in health and healthcare. Co-organized by Duke Statistical Science assistant professor Katherine Heller, PhD who is also a research scientist at Google AI, the workshop aims to facilitate collaboration between machine learning researchers, statisticians, mobile sensing researchers, human-computer interaction researchers, and clinicians from around the world.
Seven Duke +DS learning experiences will be held in September. These sessions offer the opportunity to dive deeper into topics and target diverse units at Duke: from those that desire a broad understanding of what is possible with data science, and those who wish to use data-science tools (software) without a need for deep understanding of underlying methodology, to those who desire a rigorous technical proficiency of the details and methodology of data science. Anyone in the Duke community is welcome to join, there is no fee to attend, and no prior experience is necessary.
Duke DataFest Analysis Supports Effectiveness of Social Distancing in Reducing the Spread of COVID-19
Across the world and in the United States, multiple studies have shown that social distancing is effective at reducing the spread of SARS-CoV-2 both at interpersonal and statewide levels. An early analysis of social distancing in the United States amid the COVID-19 pandemic, presented at this year’s Duke American Statistical Association (ASA) DataFest: COVID-19 Virtual Data Challenge by Duke undergraduates Shannon Houser and Jack Lichtenstein, echoed those findings and won the “Best Visualizations” prize at the contest. Using data available from Google Mobility Reports, the duo explored how factors such as population density, initial number of positive coronavirus cases per capita, governor’s political affiliation, and official shelter-in-place orders influenced the magnitude of a state’s social distancing early during the COVID-19 pandemic.
Duke DataFest Analysis Reveals How COVID-19 Impacts Communities Already Suffering from Health Disparities
Aside from altering the very fabric of daily life across the United States and the world, the COVID-19 pandemic has exposed the many existing shortcomings and inequities of the American healthcare system. The burgeoning public health crisis has resulted in more than 5 million confirmed cases nationwide and close to 163,000 deaths as of the beginning of August. However, some communities and groups have been disproportionately impacted, as a prize-winning analysis by Duke’s Meredith Brown, Matt Feder, and Pouya Mohammadi, presented at this year’s Duke American Statistical Association (ASA) DataFest: COVID-19 Virtual Data Challenge.
The Duke University School of Medicine’s Office of Data Science and Information Technology and Duke AI Health partnered to co-host a virtual symposium on Wednesday, June 24, focused on using data science to combat public health crises. In her opening remarks, School of Medicine Dean Mary E. Klotman said that we are in the midst of two pandemics—COVID-19 and racism. The School of Medicine, of which the DCRI is part, will play an active role in driving solutions to both of these pandemics, and data science is one of the key tools that will be used. The symposium, titled “Public Health Crises of 2020: Battling COVID-19 and Disparities with Data,” featured seven DCRI faculty and data science experts from a range of other Duke entities such as Duke Forge and AI Health. The event also featured speakers external to the University, including two keynote speakers from the NC Department of Health and Human Services, as well as speakers from Change Healthcare and Amazon Web Services Data Exchange. The event, which was delivered in rapid-fire five-minute talks, was hosted and moderated by the DCRI’s Michael Pencina, PhD, Vice Dean for Data Science & Information Technology (pictured left). Other DCRI speakers included Jessilyn Dunn, PhD; Benjamin Goldstein, PhD; Ricardo Henao, PhD; Keith Marsolo, PhD; Susanna Naggie, MD; and DCRI fellow Jedrek Wosik, MD.
Creativity and insight were on display as a panel of judges announced the winners of the Duke ASA DataFest: COVID-19 Virtual Data Challenge on May 5th. The contest, which took place from April 8th through April 22nd, encouraged Duke students to use data science to explore unique effects of the COVID-19 pandemic on daily life and different aspects of the social fabric of the United States. Contest participants, working alone or in teams, were prompted to use publicly available data resources to gain insights into the cultural and societal impact of the global COVID-19 pandemic. The entries were judged by a panel of 15 experts drawn from academia and industry, with prizes awarded in categories that included “Most creative topic or data set”; “Best Visualizations”; “Best Interactive Dashboard”; “Best Insight”; and a “Judges’ Pick” award to recognize achievement outside of the other categories.
BLOG: “During the onset of an event such as the one we’re now experiencing, resilience is the key priority. Secure your systems and protect your family and business. Remember, cybercrime spreads just as easily from personal devices to work devices as viruses do between people. Biodefense may have previously been considered the domain of the military and antiterrorism experts, but all of us now have a potential role to play. Please consider lending your time and expertise.” – Eric D. Perakslis, PhD
BLOG: “As we advance into the era of learning health systems, we need to systematize a process for how clinicians and data scientists can work together to solve important problems with EHR data.” – Andrew Olson, MPP, and Scott Kollins, PhD
Duke Biomedical Engineers Find Heart Rate Measurements of Wearable Monitors Vary by Activity, Not Skin Color
Biomedical engineers at Duke University have demonstrated that while different wearable technologies, like smart watches and fitness trackers, can accurately measure heart rate across a variety of skin tones, the accuracy between devices begins to vary wildly when they measure heart rate during different types of everyday activities. The study results appear online on February 10 in the journal NPJ Digital Medicine.
In collaboration with Duke AI Health and Duke Forge, the Duke Department of Medicine is seeking proposals for research in health data science. This request for proposals is designed to fulfill two missions: 1) to grow Department of Medicine faculty involvement in health data science; and 2) to support research that will then be used to improve the quality of care for patients at Duke Health.
The Department is particularly interested in proposals that utilize data from the Duke University Health System. We plan to fund several 1-year awards.
The deadline for submitting proposals has been extended to 5:00 PM Eastern time, Thursday, April 30th, 2020.
Duke Forge, in collaboration with Duke AI Health, announces a new scholarship opportunity for research at the intersection of quantitative science and health. This program is open to postdoctoral researchers in quantitative science programs. This program funds innovative, strategic, and creative researchers to develop and apply new analytical tools to solve challenges in human health and the delivery of healthcare. Successful applicants will show evidence of outstanding research ability and strong interest in health data science.
Duke Vice President for Research Lawrence Carin, PhD, and Forge Co-Director Erich Huang, MD, PhD, recently introduced AI Health, Duke’s new multidisciplinary, campus-spanning organization dedicated to enabling research into applications of artificial intelligence and machine learning in healthcare, and to effectively translating that research into techniques and technologies that will improve health outcomes for patients and communities. AI Health will also have a strong presence in education and workforce development as it builds training programs to equip students, quantitative scientists, and clinicians for a future that will increasingly be shaped by data science.